library(tidyverse)
## -- Attaching packages ---------------- tidyverse 1.3.0 --
## v ggplot2 3.3.0 v purrr 0.3.4
## v tibble 3.0.0 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## -- Conflicts ------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
Confirmed_State_3_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-13-2020.csv")) %>%
rename(Country_Region = "Country/Region", Province_State = "Province/State") %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed))
## Parsed with column specification:
## cols(
## `Province/State` = col_character(),
## `Country/Region` = col_character(),
## `Last Update` = col_datetime(format = ""),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Latitude = col_double(),
## Longitude = col_double()
## )
## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)
Confirmed_State_9_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-13-2020.csv")) %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed))
## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )
## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)
setdiff(Confirmed_State_9_13$Province_State, Confirmed_State_3_13$Province_State)
## [1] "Guam" "Northern Mariana Islands"
## [3] "Puerto Rico" "Recovered"
## [5] "Virgin Islands"
Confirmed_State_3_13_9_13_joined <- full_join(Confirmed_State_3_13,
Confirmed_State_9_13, by = c("Province_State"))
Confirmed_State_3_13_9_13_joined <- full_join(Confirmed_State_3_13,
Confirmed_State_9_13, by = c("Province_State")) %>%
rename(Confirmed_3_13_2020 = "Confirmed.x", Confirmed_9_13_2020 = "Confirmed.y") %>%
select(-Country_Region.x, -Country_Region.y) %>%
replace_na(list(Confirmed_3_13_2020 = 0))
which(is.na(Confirmed_State_3_13_9_13_joined))
## integer(0)
Confirmed_State_3_13_9_13_joined_long <- Confirmed_State_3_13_9_13_joined %>%
pivot_longer(-c(Province_State),
names_to = "Date", values_to = "Confirmed")
ggplot(Confirmed_State_3_13_9_13_joined_long, aes(x = Confirmed, y = Province_State)) +
geom_point(aes(color = Date))
time_series_confirmed <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")
## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## See spec(...) for full column specifications.
time_series_confirmed_long <- time_series_confirmed %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed")
download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
destfile = "~/UMass/time_series_covid19_deaths_global.csv")
time_series_deaths <- read_csv("~/UMass/time_series_covid19_deaths_global.csv")%>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")
## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## See spec(...) for full column specifications.
time_series_deaths_long <- time_series_deaths %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Deaths")
time_series_confirmed_long <- time_series_confirmed_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)
time_series_deaths_long <- time_series_deaths_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".") %>%
select(Key, Deaths)
time_series_long_joined <- full_join(time_series_confirmed_long,
time_series_deaths_long, by = c("Key")) %>%
select(-Key)
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
time_series_long_joined$Date <- mdy(time_series_long_joined$Date)
time_series_long_joined_counts <- time_series_long_joined %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
names_to = "Report_Type", values_to = "Counts")
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","Japan", "Korea, South",
"Italy","Spain", "US")) %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Deaths") +
facet_wrap(~Country_Region, ncol=2, scales="free_y")
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","France","Italy",
"Korea, South", "US")) %>%
ggplot(aes(x = Date, y = Deaths, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Deaths")
time_series_long_joined_counts %>%
group_by(Country_Region, Report_Type, Date) %>%
summarise(Counts = sum(Counts)) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = log2(Counts), fill = Report_Type, color = Report_Type)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Cases")
## `summarise()` regrouping output by 'Country_Region', 'Report_Type' (override with `.groups` argument)
## Repeating the Process with 6/13/20
Confirmed_State_6_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/06-13-2020.csv")) %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed))
## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )
## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)
Confirmed_State_9_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-13-2020.csv")) %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed))
## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )
## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)
setdiff(Confirmed_State_9_13$Province_State, Confirmed_State_6_13$Province_State)
## character(0)
Confirmed_State_6_13_9_13_joined <- full_join(Confirmed_State_6_13,
Confirmed_State_9_13, by = c("Province_State"))
Confirmed_State_6_13_9_13_joined <- full_join(Confirmed_State_6_13,
Confirmed_State_9_13, by = c("Province_State")) %>%
rename(Confirmed_6_13_2020 = "Confirmed.x", Confirmed_9_13_2020 = "Confirmed.y") %>%
select(-Country_Region.x, -Country_Region.y) %>%
replace_na(list(Confirmed_6_13_2020 = 0))
which(is.na(Confirmed_State_6_13_9_13_joined))
## integer(0)
Confirmed_State_6_13_9_13_joined_long <- Confirmed_State_6_13_9_13_joined %>%
pivot_longer(-c(Province_State),
names_to = "Date", values_to = "Confirmed")
ggplot(Confirmed_State_6_13_9_13_joined_long, aes(x = Confirmed, y = Province_State)) +
geom_bar(stat = 'identity', aes(color = Date)) +
labs(title = "Confirmed Cases by State", x = "Confirmed COVID Cases", y = "State")
time_series_long_joined %>%
group_by(Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("Global COVID-19 Deaths")
library(tidyverse)
library(lubridate)
time_series_confirmed_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region") %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed")
## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## See spec(...) for full column specifications.
# Let's get the times series data for deaths
time_series_deaths_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region") %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Deaths")
## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## See spec(...) for full column specifications.
# Create Keys
time_series_confirmed_long <- time_series_confirmed_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)
time_series_deaths_long <- time_series_deaths_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".") %>%
select(Key, Deaths)
# Join tables
time_series_long_joined <- full_join(time_series_confirmed_long,
time_series_deaths_long, by = c("Key")) %>%
select(-Key)
# Reformat the data
time_series_long_joined$Date <- mdy(time_series_long_joined$Date)
# Create Report table with counts
time_series_long_joined_counts <- time_series_long_joined %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
names_to = "Report_Type", values_to = "Counts")
# Plot graph to a pdf outputfile
pdf("~/UMass/time_series_example_plot.pdf", width=6, height=5)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
dev.off()
## png
## 2
# Plot graph to a png outputfile
ppi <- 300
png("C:/Users/eliaj/OneDrive/Documents/UMass/time_series_example_plot.png", width=8*ppi, height=8*ppi, res=ppi)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
dev.off()
## png
## 2
### Interactive Graphs
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplotly(
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths"))
time_series_deaths_per_confirmed <- time_series_long_joined %>%
filter(!is.na(Deaths), !is.na(Confirmed)) %>%
mutate(deaths_per_confirmed = Deaths / Confirmed)
time_series_deaths_per_confirmed %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths / Confirmed)) +
geom_point() +
geom_line()
ggtitle("US COVID-19 Deaths/Confirmed")
## $title
## [1] "US COVID-19 Deaths/Confirmed"
##
## attr(,"class")
## [1] "labels"
time_series_deaths_per_confirmed %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("Brazil","India","Mexico","UK","US","Italy","Peru", "France", "Spain", "Iran")) %>%
ggplot(aes(x = Date, y = Deaths / Confirmed, color = Country_Region)) +
geom_line()
## Warning: Removed 173 row(s) containing missing values (geom_path).
ggtitle("Top 10 Global COVID-19 Deaths/Confirmed")
## $title
## [1] "Top 10 Global COVID-19 Deaths/Confirmed"
##
## attr(,"class")
## [1] "labels"
time_series_deaths_per_confirmed %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("Brazil","India","Mexico","UK","US","Italy","Peru", "France", "Spain", "Iran")) %>%
ggplot(aes(x = Date, y = Deaths / Confirmed, color = Country_Region)) +
geom_line() +
facet_wrap(~Country_Region) +
theme_dark()
## Warning: Removed 173 row(s) containing missing values (geom_path).
ggtitle("Top 10 Global COVID-19 Deaths/Confirmed")
## $title
## [1] "Top 10 Global COVID-19 Deaths/Confirmed"
##
## attr(,"class")
## [1] "labels"
time_series_confirmed_long_states <- time_series_confirmed_long %>%
group_by(Province_State, Date) %>%
filter(Country_Region == "US") %>%
summarise_at(c("Confirmed"), sum)
ggplot(time_series_confirmed_long_states, aes(x = Date, y = Confirmed)) +
geom_line() +
ggtitle("US COVID-19 Confirmed")
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
facet_wrap(~Province_State)
## <ggproto object: Class FacetWrap, Facet, gg>
## compute_layout: function
## draw_back: function
## draw_front: function
## draw_labels: function
## draw_panels: function
## finish_data: function
## init_scales: function
## map_data: function
## params: list
## setup_data: function
## setup_params: function
## shrink: TRUE
## train_scales: function
## vars: function
## super: <ggproto object: Class FacetWrap, Facet, gg>
library(gganimate)
library(transformr)
data_time <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","Korea, South","Japan","Italy","US"))
p <- ggplot(data_time, aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("Confirmed COVID-19 Cases") +
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)